Soft soil deep foundation pit earth excavation construction scheme planning system

Abstract

The invention discloses a soft soil deep foundation pit earth excavation construction scheme planning system, relates to the technical field of construction scheme planning, and aims to effectively solve the problem that a strategy made based on a prediction result lacks certain completeness in an existing implementation mode. A prediction result of an existing prediction model is converted into a first quantitative score in an output confidence mode, then a series of compensation processing based on data driving is carried out on the first quantitative score, specifically, effective quantitative processing is carried out on uncertain factors in the earth excavation construction process of the soft soil deep foundation pit, and the prediction result of the first quantitative score is obtained. The method comprises the steps of converting a first quantitative score into a third quantitative score, combining the third quantitative score with a first quantitative score of a prediction result so as to obtain a comprehensive quantitative score of the prediction result, and finally making a construction scheme planning strategy based on the comprehensive quantitative score of the prediction result so as to effectively solve the problem that an existing strategy is lack of completeness. And the reliability of the engineering quality can be effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of construction scheme planning, and in particular to a construction scheme planning system for earthwork excavation of a soft soil deep foundation pit. Background Art

[0002] With the acceleration of urbanization, especially in coastal, riverside and lake areas, deep foundation pit projects under soft soil geological conditions are increasing. Due to the characteristics of soft soil such as high natural water content, high compressibility and low shear strength, deep foundation pit construction in such areas faces many challenges. The existing soft soil deep foundation pit earthwork excavation construction plan planning usually adopts the following technical means: first, advanced geophysical exploration technology and drilling sampling are used to obtain high-resolution geological structure information data, as well as collect environmental information data, including experience data from previous similar projects, and then the collected data is preprocessed, where the preprocessing includes noise removal, missing value processing, etc. to ensure data quality, and feature engineering operations are performed to extract key features that are helpful to the prediction model, such as soil physical property parameters, groundwater level changes, etc. Then, a deep learning model is selected, and a neural network model is usually used as the prediction model. After a series of conventional training of the prediction model using the collected data, it is put into prediction use, and finally based on the prediction results of the prediction model, the prediction results include the construction risks of soft soil deep foundation pit earthwork excavation, etc., and the corresponding construction plan planning strategy is formulated based on the prediction results; However, in the process of executing predictions, the existing prediction models usually combine actual data, such as the geological structure information data, environmental information data and experience data of previous similar projects, etc., and often ignore the uncertain factors in the excavation process of soft soil deep foundation pits, such as changes in geological conditions, the impact of extreme weather events and sudden changes in groundwater levels, etc., which ultimately leads to a lack of perfection in the strategies formulated based on the prediction results, which in turn easily affects the reliability of engineering quality. Therefore, the prior art is in urgent need of a technical solution for a soft soil deep foundation pit earthwork excavation construction plan planning system. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a soft soil deep foundation pit earthwork excavation construction plan planning system, which specifically includes the following modules: Data collection module: used to collect geological and environmental information data and historical project experience data during the excavation of soft soil deep foundation pits, and integrate them into the first data set; A first quantitative score acquisition module: connected to the data collection module, used to preprocess the collected first data set, input the preprocessed first data set into the pre-trained prediction model, and determine the first quantitative score of the prediction result based on the confidence output by the prediction model; Comprehensive quantitative score calculation module: connected to the first quantitative score acquisition module, used to perform compensation processing on the first quantitative score to obtain a comprehensive quantitative score of the prediction result; Uncertainty factor collection and classification unit: used to collect uncertainties by analyzing the historical project experience data in the first data set, and classify the collected uncertainties according to the nature of the uncertainties; Influence range value and occurrence frequency calculation unit: used to calculate the influence range value and occurrence frequency of each type of uncertain factors; Impact area definition subunit: used to define the area affected by each type of uncertainty factor using geographic information system technology; Measurement subunit: used to measure the total area of ​​the region affected by each type of uncertainty factor; Average slope factor acquisition subunit: used to calculate the slope of each point in the area affected by each type of uncertain factors using the digital elevation model, and take the average to obtain the average slope factor of each point in the area affected by each type of uncertain factors; Building density factor acquisition subunit: used to use remote sensing images to count the number of buildings in the area affected by each type of uncertain factors, and calculate the number of buildings per unit area to obtain the building density factor in the area affected by each type of uncertain factors; Impact range value calculation subunit: used to calculate the impact range value of each type of uncertain factor according to the total area of ​​the area affected by each type of uncertain factor, the average slope factor of each point in the area affected by each type of uncertain factor, and the building density factor in the area affected by each type of uncertain factor; The calculation formula for the influence range value of each type of uncertainty factor is: In the formula, Represents the impact range value of the i-th type of uncertainty factor; Represents the total area of ​​the region affected by the i-th type of uncertainty factor; Represents the average slope factor of each point in the area affected by the i-th type of uncertainty factor; Represents the building density factor in the area affected by the i-th type of uncertainty factor; , They represent the adjustment coefficients of the average slope factor and the building density factor respectively; Statistics subunit: used to count the actual number of occurrences of each type of uncertainty factor based on historical project experience data; Segmentation subunit: used to segment the actual number of occurrences of each type of uncertainty factor according to the time period; Occurrence frequency calculation subunit: used to calculate the occurrence frequency of each type of uncertainty factor according to the actual number of occurrences of each type of uncertainty factor in each time period; The calculation formula for the occurrence frequency of each type of uncertainty factor is: In the formula, Represents the occurrence frequency of the i-th type of uncertainty factor; represents the actual number of occurrences of the i-th type of uncertainty factor in the j-th time period; represents the length of the jth time period; Represents the total number of time periods; The second quantitative score calculation unit is used to comprehensively calculate the impact range value and occurrence frequency of each type of uncertainty factor to obtain the second quantitative score of each type of uncertainty factor; The calculation formula for the second quantitative score of each type of uncertainty factor is: In the formula, The second quantitative score representing the uncertainty factor of type i; Represents the impact range value of the i-th type of uncertainty factor; Represents the occurrence frequency of the i-th type of uncertainty factor; , Represent the weight coefficients of the impact range value and the occurrence frequency respectively; A third quantitative score calculation unit: used for synthesizing the second quantitative scores of each type of uncertain factors to calculate the third quantitative scores of all uncertain factors; The calculation formula for the third quantitative score of all uncertainties is: In the formula, a third quantitative score representing all uncertainties; The second quantitative score representing the uncertainty factor of type i; represents the total number of uncertainty categories; Comprehensive quantitative score calculation unit: used for combining the third quantitative scores of all uncertain factors with the first quantitative scores of the prediction results to obtain a comprehensive quantitative score of the prediction results; The calculation formula for the comprehensive quantitative score of the prediction results is: In the formula, Represents the comprehensive quantitative score of the prediction results; The first quantitative score representing the prediction result; a third quantitative score representing all uncertainties; , Represent the weight coefficients of the first quantitative score and the third quantitative score respectively; Strategy formulation module: connected with the comprehensive quantitative scoring calculation module, it is used to formulate construction plan planning strategies based on the comprehensive quantitative scoring of the prediction results.

[0004] The embodiments of the present invention have the following technical effects: The present invention aims to effectively solve the problem that strategies formulated based on prediction results in existing implementations lack a certain degree of perfection, by converting the prediction results of the existing prediction model into a first quantitative score in the form of the confidence of its output, and then performing a series of data-driven compensation processing on the first quantitative score. Specifically, by performing effective quantitative processing on the uncertain factors in the earthwork excavation construction process of soft soil deep foundation pits to convert them into a third quantitative score, and then combining the third quantitative score with the first quantitative score of the prediction result to obtain a comprehensive quantitative score of the prediction result, and finally formulating a construction plan planning strategy based on the comprehensive quantitative score of the prediction result, so as to effectively solve the problem that existing strategies lack perfection, thereby effectively improving the reliability of engineering quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0006] Figure 1 It is a framework diagram of a soft soil deep foundation pit earthwork excavation construction plan planning system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0007] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0008] Embodiment 1: Figure 1 As shown, the present invention provides a soft soil deep foundation pit earthwork excavation construction plan planning system, including the following modules: Data collection module: used to collect geological and environmental information data and historical project experience data during the excavation of soft soil deep foundation pits, and integrate them into the first data set; It is worth noting that geological information data mainly involves the underground structure and characteristics of the foundation pit and its surrounding areas, including but not limited to the following types of data: geotechnical engineering parameter data, such as the distribution of different types of soil such as silt, clay, silt sand, etc. in the foundation pit, and soil moisture content data, as well as the proportion of pore space in the soil, that is, the porosity; environmental information data covers the natural and social environmental characteristics of the construction area and its surroundings, including but not limited to the following types of data: meteorological data, such as rainfall, temperature changes, wind speed and direction, etc.; hydrological data, such as river and lake water levels, etc.; ecological and environmental protection data, such as vegetation types and coverage rates around the foundation pit, etc.

[0009] A first quantitative score acquisition module: connected to the data collection module, used to preprocess the collected first data set, input the preprocessed first data set into the pre-trained prediction model, and determine the first quantitative score of the prediction result based on the confidence output by the prediction model; It is worth noting that since the prediction model not only outputs specific prediction results, but also provides the confidence of each prediction result, the confidence is mainly used to measure the confidence level of the model in the prediction result, and the confidence interval is usually between 0 and 1. For example, when the confidence of the prediction result is greater than or equal to 0 and less than 0.5, it indicates that the model is skeptical about the prediction result. The prediction results in this interval are usually considered unreliable, so the prediction result is usually ignored directly. When the confidence of the prediction result is greater than or equal to 0.5 and less than 0.7, it indicates that the model has a certain degree of confidence in the prediction result, but there is still a large uncertainty. These results can be used as a reference, but should be treated with caution in practical applications. When the confidence of the prediction result is greater than or equal to 0.7 and less than 0.9, it indicates that the model has a high confidence in the prediction result. These prediction results are generally considered to be relatively reliable and can be used for strategy or decision support, but they still need to be treated with caution. When the confidence of the prediction result is greater than or equal to 0.9 and less than or equal to 1, it indicates that the model is very confident in the prediction result. These prediction results are generally considered to be very reliable and can be directly used for strategy or decision support. For this reason, the first quantitative score for determining the prediction result based on the confidence of the prediction model output can be directly based on the confidence of the prediction model output as the first quantitative score; It is worth further explaining that the acquisition process of the pre-trained prediction model includes: first, a large amount of geological, environmental information and various parameters in the construction process are collected from multiple completed similar projects. These data should be as comprehensive as possible, including but not limited to geological data during excavation of soft soil deep foundation pits, etc., and then the collected historical data are annotated to clarify the success or failure of each project, risk level, and other key indicators. The quality of the annotation directly affects the effect of model training. Then, the collected historical data is preprocessed, including noise removal, missing value filling, and feature extraction. Then, a neural network model is selected as the model used for prediction, such as a convolutional neural network, a recurrent neural network model, etc., and then the annotated historical data is divided into a training set, a validation set, and a test set. The general ratio is 70% training set, 15% validation set, and 15% test set. The training set is used to train the selected model. During the training process, the model parameters are continuously adjusted through the back-propagation algorithm to minimize the loss function and improve the prediction accuracy. The model performance is then evaluated using the validation set and test set, focusing on indicators such as accuracy, recall rate, and F1 score. When the model is fully trained and reaches the expected performance, it is saved for subsequent prediction applications.

[0010] Comprehensive quantitative score calculation module: connected to the first quantitative score acquisition module, used to perform compensation processing on the first quantitative score to obtain a comprehensive quantitative score of the prediction result; Uncertainty factor collection and classification unit: used to collect uncertainties by analyzing the historical project experience data in the first data set, and classify the collected uncertainties according to the nature of the uncertainties; It is worth noting that the nature of uncertainties can be man-made and non-man-made factors, and uncertainties include but are not limited to: changes in geological conditions, the impact of extreme weather events, and sudden changes in groundwater levels, among which changes in geological conditions can include soil settlement and soil compression; the impact of extreme weather events can include rainstorms and typhoons; and sudden changes in groundwater levels can include groundwater level rise and groundwater level drop. It is worth further explaining that by classifying uncertainties, different types of uncertainties can be identified and distinguished more finely. Each type has different impact mechanisms and characteristics. After classification, more precise analysis can be performed on specific types. Influence range value and occurrence frequency calculation unit: used to calculate the influence range value and occurrence frequency of each type of uncertain factors; Impact area definition subunit: used to define the area affected by each type of uncertainty factor using geographic information system technology; Measurement subunit: used to measure the total area of ​​the region affected by each type of uncertainty factor; Average slope factor acquisition subunit: used to calculate the slope of each point in the area affected by each type of uncertain factors using the digital elevation model, and take the average to obtain the average slope factor of each point in the area affected by each type of uncertain factors; It is worth mentioning that the digital elevation model is a data set that represents the ground elevation, usually stored in a regular grid format. Each grid cell contains a value representing the surface height at that location. In geographic information systems, digital elevation models are widely used for terrain analysis, flood simulation, viewshed analysis, etc. The center of each grid cell in the digital elevation model is called a grid node, which has clear coordinates and corresponding elevation values. For the slope calculation method, the slope can be estimated by the height difference of its surrounding neighboring nodes. Common slope calculation methods include the neighborhood method and the gradient method. The neighborhood method uses the central node and its eight surrounding neighboring nodes to calculate the slope, while the gradient method is based on the differential principle and directly extracts the slope value from the digital elevation model data. This slope calculation method is relatively conventional and will not be described here one by one.

[0011] Building density factor acquisition subunit: used to use remote sensing images to count the number of buildings in the area affected by each type of uncertain factors, and calculate the number of buildings per unit area to obtain the building density factor in the area affected by each type of uncertain factors; Impact range value calculation subunit: used to calculate the impact range value of each type of uncertain factor according to the total area of ​​the area affected by each type of uncertain factor, the average slope factor of each point in the area affected by each type of uncertain factor, and the building density factor in the area affected by each type of uncertain factor; The calculation formula for obtaining the influence range value of each type of uncertainty factor is: In the formula, Represents the impact range value of the i-th type of uncertainty factor; Represents the total area of ​​the region affected by the i-th type of uncertainty factor; Represents the average slope factor of each point in the area affected by the i-th type of uncertainty factor; Represents the building density factor in the area affected by the i-th type of uncertainty factor; , Represents the adjustment coefficients of average slope factor and building density factor respectively; Statistical subunit: used to count the actual number of occurrences of each type of uncertain factors based on historical project experience data; Segmentation subunit: used to segment the actual number of occurrences of each type of uncertainty factor according to the time period; Occurrence frequency calculation subunit: used to calculate the occurrence frequency of each type of uncertainty factor according to the actual number of occurrences of each type of uncertainty factor in each time period; Among them, the calculation formula for the occurrence frequency of each type of uncertainty factor is: In the formula, Represents the occurrence frequency of the i-th type of uncertainty factor; represents the actual number of occurrences of the i-th type of uncertainty factor in the j-th time period; represents the length of the jth time period; Represents the total number of time periods; The second quantitative score calculation unit is used to comprehensively calculate the impact range value and occurrence frequency of each type of uncertainty factor to obtain the second quantitative score of each type of uncertainty factor; The calculation formula for obtaining the second quantitative score of each type of uncertainty factor is: In the formula, The second quantitative score representing the uncertainty factor of type i; Represents the impact range value of the i-th type of uncertainty factor; Represents the occurrence frequency of the i-th type of uncertainty factor; , Represent the weight coefficients of the impact range value and the occurrence frequency respectively; A third quantitative score calculation unit: used for synthesizing the second quantitative scores of each type of uncertain factors to calculate the third quantitative scores of all uncertain factors; The calculation formula for obtaining the third quantitative score of all uncertain factors is: In the formula, Represents the occurrence frequency of the i-th type of uncertainty factor; represents the actual number of occurrences of the i-th type of uncertainty factor in the j-th time period; represents the length of the jth time period; Represents the total number of time periods; The second quantitative score calculation unit is used to comprehensively calculate the impact range value and occurrence frequency of each type of uncertainty factor to obtain the second quantitative score of each type of uncertainty factor; The calculation formula for obtaining the second quantitative score of each type of uncertainty factor is: In the formula, The second quantitative score representing the uncertainty factor of type i; Represents the impact range value of the i-th type of uncertainty factor; Represents the occurrence frequency of the i-th type of uncertainty factor; , Represent the weight coefficients of the impact range value and the occurrence frequency respectively; A third quantitative score calculation unit: used for synthesizing the second quantitative scores of each type of uncertain factors to calculate the third quantitative scores of all uncertain factors; The calculation formula for obtaining the third quantitative score of all uncertain factors is: In the formula, a third quantitative score representing all uncertainties; The second quantitative score representing the uncertainty factor of type i; represents the total number of uncertainty categories; It is worth noting that by classifying uncertain factors, a more accurate analysis can be performed on specific types, thereby effectively ensuring the accuracy of the second quantitative score and reducing calculation errors. This effectively guarantees the accuracy and reliability of the third quantitative score obtained by the final calculation.

[0012] Comprehensive quantitative score calculation unit: used for combining the third quantitative scores of all uncertain factors with the first quantitative scores of the prediction results to obtain a comprehensive quantitative score of the prediction results; Among them, the calculation formula for the comprehensive quantitative score of the prediction result is: In the formula, Represents the comprehensive quantitative score of the prediction results; The first quantitative score representing the prediction result; a third quantitative score representing all uncertainties; , Represent the weight coefficients of the first quantitative score and the third quantitative score respectively; Strategy formulation module: connected with the comprehensive quantitative scoring calculation module, used to formulate construction plan planning strategies based on the comprehensive quantitative scoring of the prediction results; For example, when the prediction result shows that there is a thick layer of silty soil at the bottom of the foundation pit with low shear strength, and based on the above calculation process, the comprehensive quantitative score of the prediction result is 0.71, then in view of the comprehensive quantitative score of this prediction result, it shows that the construction of the foundation pit project faces a higher risk, so it is necessary to adopt a more cautious and comprehensive construction plan planning strategy. The corresponding strategies that can be adopted are: considering the low shear strength of the silty soil layer at the bottom of the foundation pit, a more solid support structure is required, such as increasing the number of anchor rods or using steel sheet piles for reinforcement to ensure the stability of the slope, and inviting geological experts and structural engineers to participate in the design stage to optimize the support plan to ensure that it can withstand possible geological changes.

[0013] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0014] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0015] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A soft soil deep foundation pit earthwork excavation construction plan planning system, characterized in that: Includes the following modules: Data collection module: used to collect geological and environmental information data and historical project experience data during the excavation of soft soil deep foundation pits, and integrate them into the first data set; A first quantitative score acquisition module: connected to the data collection module, used to preprocess the collected first data set, input the preprocessed first data set into the pre-trained prediction model, and determine the first quantitative score of the prediction result based on the confidence output by the prediction model; Comprehensive quantitative score calculation module: connected to the first quantitative score acquisition module, used to perform compensation processing on the first quantitative score to obtain a comprehensive quantitative score of the prediction result; Strategy formulation module: connected with the comprehensive quantitative scoring calculation module, it is used to formulate construction plan planning strategies based on the comprehensive quantitative scoring of the prediction results.

2. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 1, characterized in that: The performing of compensation processing on the first quantitative score to obtain a comprehensive quantitative score of the prediction result includes: Uncertainty factor collection and classification unit: used to collect uncertainties by analyzing the historical project experience data in the first data set, and classify the collected uncertainties according to the nature of the uncertainties; Influence range value and occurrence frequency calculation unit: used to calculate the influence range value and occurrence frequency of each type of uncertain factors; The second quantitative score calculation unit is used to comprehensively calculate the impact range value and occurrence frequency of each type of uncertainty factor to obtain the second quantitative score of each type of uncertainty factor; A third quantitative score calculation unit: used for synthesizing the second quantitative scores of each type of uncertain factors to calculate the third quantitative scores of all uncertain factors; Comprehensive quantitative score calculation unit: used to combine the third quantitative scores of all uncertain factors with the first quantitative scores of the prediction results to obtain a comprehensive quantitative score of the prediction results.

3. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 2, characterized in that: The calculation of the impact range value of each type of uncertainty factor includes: Impact area definition subunit: used to define the area affected by each type of uncertainty factor using geographic information system technology; Measurement subunit: used to measure the total area of ​​the region affected by each type of uncertainty factor; Average slope factor acquisition subunit: used to calculate the slope of each point in the area affected by each type of uncertain factors using the digital elevation model, and take the average to obtain the average slope factor of each point in the area affected by each type of uncertain factors; Building density factor acquisition subunit: used to use remote sensing images to count the number of buildings in the area affected by each type of uncertain factors, and calculate the number of buildings per unit area to obtain the building density factor in the area affected by each type of uncertain factors; Impact range value calculation subunit: used to calculate the impact range value of each type of uncertain factor based on the total area of ​​the area affected by each type of uncertain factor, the average slope factor of each point in the area affected by each type of uncertain factor, and the building density factor in the area affected by each type of uncertain factor.

4. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 3, characterized in that: The calculation of the occurrence frequency of each type of uncertainty factor includes: Statistics subunit: used to count the actual number of occurrences of each type of uncertainty factor based on historical project experience data; Segmentation subunit: used to segment the actual number of occurrences of each type of uncertainty factor according to the time period; Occurrence frequency calculation subunit: used to calculate the occurrence frequency of each type of uncertainty factor according to the actual number of occurrences of each type of uncertainty factor in each time period.

5. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 4, characterized in that: The calculation formula for the influence range value of each type of uncertainty factor is: In the formula, Represents the impact range value of the i-th type of uncertainty factor; Represents the total area of ​​the region affected by the i-th type of uncertainty factor; Represents the average slope factor of each point in the area affected by the i-th type of uncertainty factor; Represents the building density factor in the area affected by the i-th type of uncertainty factor; , They represent the adjustment coefficients of average slope factor and building density factor respectively.

6. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 5, characterized in that: The calculation formula for the occurrence frequency of each type of uncertainty factor is: In the formula, Represents the occurrence frequency of the i-th type of uncertainty factor; represents the actual number of occurrences of the i-th type of uncertainty factor in the j-th time period; represents the length of the jth time period; Represents the total number of time periods.

7. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 6, characterized in that: The calculation formula of the second quantitative score of each type of uncertainty factor is: In the formula, The second quantitative score representing the uncertainty factor of type i; Represents the impact range value of the i-th type of uncertainty factor; Represents the occurrence frequency of the i-th type of uncertainty factor; , They represent the weight coefficients of the impact range value and the occurrence frequency respectively.

8. The soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 7, characterized in that: The calculation formula of the third quantitative score of all uncertain factors is: In the formula, a third quantitative score representing all uncertainties; The second quantitative score representing the uncertainty factor of type i; Represents the total number of uncertainty categories.

9. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 8, characterized in that: The calculation formula of the comprehensive quantitative score of the prediction result is: In the formula, Represents the comprehensive quantitative score of the prediction results; The first quantitative score representing the prediction result; a third quantitative score representing all uncertainties; , Represent the weight coefficients of the first quantitative score and the third quantitative score respectively.

Images